Name
Abstract | ||
|---|---|---|
•This paper proposes a CNN-based segmentation framework multiscale dense CNN (MDCNN) to automatically segment cerebral vessel in DSA images.•In the training process, a patch selection strategy is put forward to get enough training data from the limited quantity of annotated cerebrovascular DSA images.•In order to verify the validity of the proposed method, we label a DSA cerebrovascular dataset DCVessel. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1016/j.neucom.2019.10.035 | Neurocomputing |
Keywords | Field | DocType |
Convolutional neural network,Digital subtraction angiography,Multiscale,Cerebrovascular Segmentation | Vessel segmentation,Digital subtraction angiography,F1 score,Pattern recognition,Convolutional neural network,Segmentation,Image segmentation,Artificial intelligence,Encoder,Clinical diagnosis,Mathematics | Journal |
Volume | ISSN | Citations |
373 | 0925-2312 | 1 |
PageRank | References | Authors |
0.40 | 0 | 6 |